Model-Based Navigation Control for Communication-Aware Motion Using Harmonic Potential Fields

This paper suggests an optimal and provably-correct method for inducing communication-aware, context-sensitive mobility in an autonomous agent. The method can fuse crisp representations of arbitrarily shaped hard-obstacles with soft representations of the wireless signal strength field that forms within the confines of the hard-obstacles. The procedure can produce a navigation control signal able to induce in a wide class of realistic agents optimal, communication-aware, dynamically-friendly trajectories to a specified target point in the environment. The approach is developed with the aid of the potential-field method for motion planning. Its immediate goal is to tackle the model-based case where the navigator assumes full knowledge of the obstacles and Wireless Communication Link (WCL). The treatment in this part provides the theoretical foundations and the tools necessary to extend the method to the sensor-based case where the agent does not a-priori know the obstacles or the WCL which have to be sensed online. Theoretical proofs of the communication-aware navigator’s performance are provided and its behavior is verified using intensive realistic simulation. Note to Practitioners—In this paper we target applications like search and rescue, where mobile agents may be utilized for information gathering and mapping purposes in regions that are hazardous to humans. Our proposed approach can simultaneously combine obstacle information and signal strength information to generate guidance signals. Trajectories manifested using these guidance signals can avoid regions with very low signal strength. Typically, guidance signals from a planner need to be smoothed before generating actuation signals. Our proposed planner provides guidance signal that can be directly used to generate actuation signals. Also the proposed approach results in trajectories that are very smooth (infinitely differentiable), therefore, energy consumption due to motion is reduced. Simulations show the effectiveness of our approach. In future work, we intend to use real-time signal strength and obstacle information data in out approach.